Overview

Brought to you by YData

Dataset statistics

Number of variables25
Number of observations1296675
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory178.1 MiB
Average record size in memory144.0 B

Variable types

Numeric21
Categorical4

Alerts

amt is highly overall correlated with amt_day_interaction and 2 other fieldsHigh correlation
amt_day_interaction is highly overall correlated with amt and 2 other fieldsHigh correlation
amt_merchant_interaction is highly overall correlated with amt and 2 other fieldsHigh correlation
amt_ratio is highly overall correlated with amt and 2 other fieldsHigh correlation
day is highly overall correlated with day_of_monthHigh correlation
day_of_month is highly overall correlated with dayHigh correlation
day_of_week is highly overall correlated with amt_day_interaction and 1 other fieldsHigh correlation
is_weekend is highly overall correlated with day_of_weekHigh correlation
merchant_encoded is highly overall correlated with amt_merchant_interactionHigh correlation
is_fraud is highly imbalanced (94.9%)Imbalance
high_value is highly imbalanced (71.4%)Imbalance
amt is highly skewed (γ1 = 42.27787379)Skewed
amt_ratio is highly skewed (γ1 = 34.80955403)Skewed
amt_merchant_interaction is highly skewed (γ1 = 50.34670773)Skewed
amt_day_interaction is highly skewed (γ1 = 62.28714456)Skewed
day_of_week has 254282 (19.6%) zerosZeros
amt_day_interaction has 254282 (19.6%) zerosZeros

Reproduction

Analysis started2024-09-15 09:50:41.454016
Analysis finished2024-09-15 09:54:01.779859
Duration3 minutes and 20.33 seconds
Software versionydata-profiling vv4.10.0
Download configurationconfig.json

Variables

amt
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct52928
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.351035
Minimum1
Maximum28948.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.9 MiB
2024-09-15T15:24:01.872957image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.44
Q19.65
median47.52
Q383.14
95-th percentile196.31
Maximum28948.9
Range28947.9
Interquartile range (IQR)73.49

Descriptive statistics

Standard deviation160.31604
Coefficient of variation (CV)2.2788014
Kurtosis4545.645
Mean70.351035
Median Absolute Deviation (MAD)37.5
Skewness42.277874
Sum91222429
Variance25701.232
MonotonicityNot monotonic
2024-09-15T15:24:02.028821image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.14 542
 
< 0.1%
1.04 538
 
< 0.1%
1.25 535
 
< 0.1%
1.02 533
 
< 0.1%
1.01 523
 
< 0.1%
1.05 519
 
< 0.1%
1.2 516
 
< 0.1%
1.23 515
 
< 0.1%
1.08 512
 
< 0.1%
1.11 509
 
< 0.1%
Other values (52918) 1291433
99.6%
ValueCountFrequency (%)
1 222
< 0.1%
1.01 523
< 0.1%
1.02 533
< 0.1%
1.03 499
< 0.1%
1.04 538
< 0.1%
1.05 519
< 0.1%
1.06 471
< 0.1%
1.07 498
< 0.1%
1.08 512
< 0.1%
1.09 496
< 0.1%
ValueCountFrequency (%)
28948.9 1
< 0.1%
27390.12 1
< 0.1%
27119.77 1
< 0.1%
26544.12 1
< 0.1%
25086.94 1
< 0.1%
17897.24 1
< 0.1%
15305.95 1
< 0.1%
15047.03 1
< 0.1%
15034.18 1
< 0.1%
14849.74 1
< 0.1%

first
Real number (ℝ)

Distinct352
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean180.26237
Minimum0
Maximum351
Zeros4571
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size4.9 MiB
2024-09-15T15:24:02.179085image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile14
Q194
median183
Q3257
95-th percentile329
Maximum351
Range351
Interquartile range (IQR)163

Descriptive statistics

Standard deviation97.533793
Coefficient of variation (CV)0.54106574
Kurtosis-1.0294248
Mean180.26237
Median Absolute Deviation (MAD)80
Skewness-0.14559279
Sum2.3374171 × 108
Variance9512.8408
MonotonicityNot monotonic
2024-09-15T15:24:02.329481image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
74 26669
 
2.1%
281 21667
 
1.7%
166 20581
 
1.6%
152 20039
 
1.5%
243 20009
 
1.5%
93 19965
 
1.5%
162 16940
 
1.3%
348 16371
 
1.3%
231 16346
 
1.3%
174 16325
 
1.3%
Other values (342) 1101763
85.0%
ValueCountFrequency (%)
0 4571
0.4%
1 9844
0.8%
2 1027
 
0.1%
3 4582
0.4%
4 525
 
< 0.1%
5 1056
 
0.1%
6 6123
0.5%
7 2049
 
0.2%
8 5630
0.4%
9 1540
 
0.1%
ValueCountFrequency (%)
351 2018
 
0.2%
350 1516
 
0.1%
349 1038
 
0.1%
348 16371
1.3%
347 1466
 
0.1%
346 533
 
< 0.1%
345 3059
 
0.2%
344 538
 
< 0.1%
343 529
 
< 0.1%
342 6695
0.5%

last
Real number (ℝ)

Distinct481
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean250.52092
Minimum0
Maximum480
Zeros517
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size4.9 MiB
2024-09-15T15:24:02.484546image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile28
Q1138
median252
Q3370
95-th percentile463
Maximum480
Range480
Interquartile range (IQR)232

Descriptive statistics

Standard deviation136.67406
Coefficient of variation (CV)0.54555946
Kurtosis-1.1380827
Mean250.52092
Median Absolute Deviation (MAD)116
Skewness-0.064789705
Sum3.2484422 × 108
Variance18679.798
MonotonicityNot monotonic
2024-09-15T15:24:02.637255image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
404 28794
 
2.2%
467 23605
 
1.8%
99 21910
 
1.7%
217 20034
 
1.5%
370 17394
 
1.3%
260 14805
 
1.1%
219 13976
 
1.1%
245 12753
 
1.0%
161 11799
 
0.9%
288 11698
 
0.9%
Other values (471) 1119907
86.4%
ValueCountFrequency (%)
0 517
 
< 0.1%
1 1565
 
0.1%
2 518
 
< 0.1%
3 1553
 
0.1%
4 501
 
< 0.1%
5 8185
0.6%
6 485
 
< 0.1%
7 8210
0.6%
8 1548
 
0.1%
9 504
 
< 0.1%
ValueCountFrequency (%)
480 502
 
< 0.1%
479 3585
0.3%
478 3071
0.2%
477 3635
0.3%
476 2578
0.2%
475 4051
0.3%
474 1521
 
0.1%
473 4656
0.4%
472 2049
0.2%
471 1029
 
0.1%

gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.9 MiB
0
709863 
1
586812 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1296675
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 709863
54.7%
1 586812
45.3%

Length

2024-09-15T15:24:02.772883image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-15T15:24:02.878510image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 709863
54.7%
1 586812
45.3%

Most occurring characters

ValueCountFrequency (%)
0 709863
54.7%
1 586812
45.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1296675
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 709863
54.7%
1 586812
45.3%

Most occurring scripts

ValueCountFrequency (%)
Common 1296675
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 709863
54.7%
1 586812
45.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1296675
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 709863
54.7%
1 586812
45.3%

street
Real number (ℝ)

Distinct983
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean488.03441
Minimum0
Maximum982
Zeros2602
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size4.9 MiB
2024-09-15T15:24:03.007567image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile48
Q1252
median485
Q3720
95-th percentile929
Maximum982
Range982
Interquartile range (IQR)468

Descriptive statistics

Standard deviation280.06084
Coefficient of variation (CV)0.57385469
Kurtosis-1.1723021
Mean488.03441
Median Absolute Deviation (MAD)235
Skewness0.0082662276
Sum6.3282202 × 108
Variance78434.073
MonotonicityNot monotonic
2024-09-15T15:24:03.168701image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 3123
 
0.2%
859 3123
 
0.2%
814 3119
 
0.2%
472 3117
 
0.2%
805 3113
 
0.2%
303 3112
 
0.2%
160 3110
 
0.2%
848 3107
 
0.2%
417 3106
 
0.2%
601 3101
 
0.2%
Other values (973) 1265544
97.6%
ValueCountFrequency (%)
0 2602
0.2%
1 1523
0.1%
2 972
 
0.1%
3 11
 
< 0.1%
4 1019
 
0.1%
5 1542
0.1%
6 3123
0.2%
7 524
 
< 0.1%
8 2032
0.2%
9 2036
0.2%
ValueCountFrequency (%)
982 1035
0.1%
981 1022
0.1%
980 521
 
< 0.1%
979 504
 
< 0.1%
978 8
 
< 0.1%
977 2553
0.2%
976 1518
0.1%
975 2021
0.2%
974 998
 
0.1%
973 523
 
< 0.1%

city
Real number (ℝ)

Distinct894
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean445.26328
Minimum0
Maximum893
Zeros532
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size4.9 MiB
2024-09-15T15:24:03.312151image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile46
Q1224
median439
Q3677
95-th percentile843
Maximum893
Range893
Interquartile range (IQR)453

Descriptive statistics

Standard deviation258.60012
Coefficient of variation (CV)0.58078024
Kurtosis-1.2142164
Mean445.26328
Median Absolute Deviation (MAD)225
Skewness0.0094241413
Sum5.7736177 × 108
Variance66874.021
MonotonicityNot monotonic
2024-09-15T15:24:03.456515image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
74 5617
 
0.4%
725 5130
 
0.4%
821 5105
 
0.4%
644 5075
 
0.4%
501 5060
 
0.4%
796 4634
 
0.4%
171 4613
 
0.4%
154 4604
 
0.4%
837 4599
 
0.4%
359 4168
 
0.3%
Other values (884) 1248070
96.3%
ValueCountFrequency (%)
0 532
 
< 0.1%
1 2097
0.2%
2 516
 
< 0.1%
3 2035
0.2%
4 511
 
< 0.1%
5 1056
0.1%
6 1025
0.1%
7 1019
0.1%
8 1034
0.1%
9 1049
0.1%
ValueCountFrequency (%)
893 1537
0.1%
892 1557
0.1%
891 525
 
< 0.1%
890 2109
0.2%
889 514
 
< 0.1%
888 511
 
< 0.1%
887 537
 
< 0.1%
886 1055
0.1%
885 1040
0.1%
884 11
 
< 0.1%

state
Real number (ℝ)

Distinct51
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.677282
Minimum0
Maximum50
Zeros2120
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size4.9 MiB
2024-09-15T15:24:03.607272image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q115
median28
Q338
95-th percentile48
Maximum50
Range50
Interquartile range (IQR)23

Descriptive statistics

Standard deviation14.330978
Coefficient of variation (CV)0.53719783
Kurtosis-1.123464
Mean26.677282
Median Absolute Deviation (MAD)12
Skewness-0.21677598
Sum34591765
Variance205.37694
MonotonicityNot monotonic
2024-09-15T15:24:03.771314image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
43 94876
 
7.3%
34 83501
 
6.4%
38 79847
 
6.2%
4 56360
 
4.3%
35 46480
 
3.6%
22 46154
 
3.6%
14 43252
 
3.3%
9 42671
 
3.3%
1 40989
 
3.2%
24 38403
 
3.0%
Other values (41) 724142
55.8%
ValueCountFrequency (%)
0 2120
 
0.2%
1 40989
3.2%
2 31127
2.4%
3 10770
 
0.8%
4 56360
4.3%
5 13880
 
1.1%
6 7702
 
0.6%
7 3613
 
0.3%
8 9
 
< 0.1%
9 42671
3.3%
ValueCountFrequency (%)
50 19322
 
1.5%
49 25691
 
2.0%
48 29368
 
2.3%
47 18924
 
1.5%
46 11768
 
0.9%
45 29250
 
2.3%
44 10699
 
0.8%
43 94876
7.3%
42 17554
 
1.4%
41 12324
 
1.0%

job
Real number (ℝ)

Distinct494
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean251.17343
Minimum0
Maximum493
Zeros1041
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size4.9 MiB
2024-09-15T15:24:03.918427image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile30
Q1131
median251
Q3374
95-th percentile469
Maximum493
Range493
Interquartile range (IQR)243

Descriptive statistics

Standard deviation140.10937
Coefficient of variation (CV)0.55781922
Kurtosis-1.1957791
Mean251.17343
Median Absolute Deviation (MAD)122
Skewness-0.014278309
Sum3.2569031 × 108
Variance19630.635
MonotonicityNot monotonic
2024-09-15T15:24:04.068509image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
193 9779
 
0.8%
187 9199
 
0.7%
308 8684
 
0.7%
439 8680
 
0.7%
286 8270
 
0.6%
121 8225
 
0.6%
444 7700
 
0.6%
239 7679
 
0.6%
194 7659
 
0.6%
178 7547
 
0.6%
Other values (484) 1213253
93.6%
ValueCountFrequency (%)
0 1041
 
0.1%
1 11
 
< 0.1%
2 534
 
< 0.1%
3 2580
0.2%
4 4673
0.4%
5 1577
 
0.1%
6 3604
0.3%
7 535
 
< 0.1%
8 2524
0.2%
9 2571
0.2%
ValueCountFrequency (%)
493 504
 
< 0.1%
492 2601
0.2%
491 2556
0.2%
490 510
 
< 0.1%
489 6164
0.5%
488 2538
0.2%
487 7
 
< 0.1%
486 3049
0.2%
485 2024
 
0.2%
484 998
 
0.1%

unix_time
Real number (ℝ)

Distinct1274823
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3492436 × 109
Minimum1.325376 × 109
Maximum1.3718168 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.9 MiB
2024-09-15T15:24:04.210928image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1.325376 × 109
5-th percentile1.328672 × 109
Q11.3387507 × 109
median1.3492497 × 109
Q31.3593854 × 109
95-th percentile1.3698306 × 109
Maximum1.3718168 × 109
Range46440799
Interquartile range (IQR)20634633

Descriptive statistics

Standard deviation12841278
Coefficient of variation (CV)0.0095173904
Kurtosis-1.0875405
Mean1.3492436 × 109
Median Absolute Deviation (MAD)10358807
Skewness0.0033779498
Sum1.7495305 × 1015
Variance1.6489843 × 1014
MonotonicityIncreasing
2024-09-15T15:24:04.363508image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1370177227 4
 
< 0.1%
1335110521 4
 
< 0.1%
1370050667 4
 
< 0.1%
1367602155 3
 
< 0.1%
1364686521 3
 
< 0.1%
1369587838 3
 
< 0.1%
1337306743 3
 
< 0.1%
1343668520 3
 
< 0.1%
1341944714 3
 
< 0.1%
1340650327 3
 
< 0.1%
Other values (1274813) 1296642
> 99.9%
ValueCountFrequency (%)
1325376018 1
< 0.1%
1325376044 1
< 0.1%
1325376051 1
< 0.1%
1325376076 1
< 0.1%
1325376186 1
< 0.1%
1325376248 1
< 0.1%
1325376282 1
< 0.1%
1325376308 1
< 0.1%
1325376318 1
< 0.1%
1325376361 1
< 0.1%
ValueCountFrequency (%)
1371816817 1
< 0.1%
1371816816 1
< 0.1%
1371816752 1
< 0.1%
1371816739 1
< 0.1%
1371816728 1
< 0.1%
1371816696 1
< 0.1%
1371816683 1
< 0.1%
1371816656 1
< 0.1%
1371816562 1
< 0.1%
1371816522 1
< 0.1%

merch_lat
Real number (ℝ)

Distinct1247805
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.537338
Minimum19.027785
Maximum67.510267
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.9 MiB
2024-09-15T15:24:04.518497image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum19.027785
5-th percentile29.751653
Q134.733572
median39.36568
Q341.957164
95-th percentile46.00353
Maximum67.510267
Range48.482482
Interquartile range (IQR)7.223592

Descriptive statistics

Standard deviation5.1097884
Coefficient of variation (CV)0.13259318
Kurtosis0.79599391
Mean38.537338
Median Absolute Deviation (MAD)3.397536
Skewness-0.18191543
Sum49970403
Variance26.109937
MonotonicityNot monotonic
2024-09-15T15:24:04.664884image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41.305966 4
 
< 0.1%
41.937796 4
 
< 0.1%
42.265012 4
 
< 0.1%
41.301611 4
 
< 0.1%
34.134994 4
 
< 0.1%
37.669788 4
 
< 0.1%
39.348185 4
 
< 0.1%
32.64469 4
 
< 0.1%
42.749184 4
 
< 0.1%
38.050673 4
 
< 0.1%
Other values (1247795) 1296635
> 99.9%
ValueCountFrequency (%)
19.027785 1
< 0.1%
19.027804 1
< 0.1%
19.029798 1
< 0.1%
19.031242 1
< 0.1%
19.032277 1
< 0.1%
19.033288 1
< 0.1%
19.034282 1
< 0.1%
19.034687 1
< 0.1%
19.035472 1
< 0.1%
19.036312 1
< 0.1%
ValueCountFrequency (%)
67.510267 1
< 0.1%
67.441518 1
< 0.1%
67.397018 1
< 0.1%
67.188111 1
< 0.1%
67.064277 1
< 0.1%
66.835174 1
< 0.1%
66.682905 1
< 0.1%
66.67355 1
< 0.1%
66.664673 1
< 0.1%
66.659242 1
< 0.1%

merch_long
Real number (ℝ)

Distinct1275745
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-90.226465
Minimum-166.67124
Maximum-66.950902
Zeros0
Zeros (%)0.0%
Negative1296675
Negative (%)100.0%
Memory size9.9 MiB
2024-09-15T15:24:04.814767image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-166.67124
5-th percentile-119.33009
Q1-96.897276
median-87.438392
Q3-80.236796
95-th percentile-73.354218
Maximum-66.950902
Range99.72034
Interquartile range (IQR)16.660479

Descriptive statistics

Standard deviation13.771091
Coefficient of variation (CV)-0.15262806
Kurtosis1.8484792
Mean-90.226465
Median Absolute Deviation (MAD)8.227889
Skewness-1.1469599
Sum-1.169944 × 108
Variance189.64294
MonotonicityNot monotonic
2024-09-15T15:24:04.962468image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-87.116414 4
 
< 0.1%
-81.219189 4
 
< 0.1%
-74.618269 4
 
< 0.1%
-85.326323 3
 
< 0.1%
-84.890305 3
 
< 0.1%
-88.49309 3
 
< 0.1%
-84.100102 3
 
< 0.1%
-97.527227 3
 
< 0.1%
-85.3444 3
 
< 0.1%
-86.037494 3
 
< 0.1%
Other values (1275735) 1296642
> 99.9%
ValueCountFrequency (%)
-166.671242 1
< 0.1%
-166.670132 1
< 0.1%
-166.669638 1
< 0.1%
-166.666179 1
< 0.1%
-166.664828 1
< 0.1%
-166.662888 1
< 0.1%
-166.661968 1
< 0.1%
-166.659277 1
< 0.1%
-166.657834 1
< 0.1%
-166.657174 1
< 0.1%
ValueCountFrequency (%)
-66.950902 1
< 0.1%
-66.955996 1
< 0.1%
-66.95654 1
< 0.1%
-66.958659 1
< 0.1%
-66.958751 1
< 0.1%
-66.959178 1
< 0.1%
-66.961923 1
< 0.1%
-66.962913 1
< 0.1%
-66.963918 1
< 0.1%
-66.963975 1
< 0.1%

is_fraud
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.9 MiB
0
1289169 
1
 
7506

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1296675
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1289169
99.4%
1 7506
 
0.6%

Length

2024-09-15T15:24:05.096170image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-15T15:24:05.200881image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 1289169
99.4%
1 7506
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 1289169
99.4%
1 7506
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1296675
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1289169
99.4%
1 7506
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common 1296675
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1289169
99.4%
1 7506
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1296675
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1289169
99.4%
1 7506
 
0.6%

day
Real number (ℝ)

HIGH CORRELATION 

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.587978
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.9 MiB
2024-09-15T15:24:05.306696image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median15
Q323
95-th percentile30
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.8291214
Coefficient of variation (CV)0.5664058
Kurtosis-1.1871417
Mean15.587978
Median Absolute Deviation (MAD)8
Skewness0.030847364
Sum20212542
Variance77.953384
MonotonicityNot monotonic
2024-09-15T15:24:05.437983image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
1 47089
 
3.6%
15 46213
 
3.6%
8 46201
 
3.6%
16 44894
 
3.5%
2 44748
 
3.5%
9 44685
 
3.4%
7 44239
 
3.4%
14 44015
 
3.4%
28 43470
 
3.4%
17 42272
 
3.3%
Other values (21) 848849
65.5%
ValueCountFrequency (%)
1 47089
3.6%
2 44748
3.5%
3 41842
3.2%
4 41479
3.2%
5 41886
3.2%
6 41420
3.2%
7 44239
3.4%
8 46201
3.6%
9 44685
3.4%
10 41934
3.2%
ValueCountFrequency (%)
31 24701
1.9%
30 41019
3.2%
29 39617
3.1%
28 43470
3.4%
27 39684
3.1%
26 40692
3.1%
25 40374
3.1%
24 41360
3.2%
23 40815
3.1%
22 42061
3.2%

day_of_week
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0706037
Minimum0
Maximum6
Zeros254282
Zeros (%)19.6%
Negative0
Negative (%)0.0%
Memory size4.9 MiB
2024-09-15T15:24:05.551671image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.1981526
Coefficient of variation (CV)0.71586984
Kurtosis-1.445049
Mean3.0706037
Median Absolute Deviation (MAD)2
Skewness-0.078453041
Sum3981575
Variance4.8318747
MonotonicityNot monotonic
2024-09-15T15:24:05.656581image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 254282
19.6%
6 250579
19.3%
5 200957
15.5%
1 160227
12.4%
4 152272
11.7%
3 147285
11.4%
2 131073
10.1%
ValueCountFrequency (%)
0 254282
19.6%
1 160227
12.4%
2 131073
10.1%
3 147285
11.4%
4 152272
11.7%
5 200957
15.5%
6 250579
19.3%
ValueCountFrequency (%)
6 250579
19.3%
5 200957
15.5%
4 152272
11.7%
3 147285
11.4%
2 131073
10.1%
1 160227
12.4%
0 254282
19.6%

age
Real number (ℝ)

Distinct81
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.742545
Minimum15
Maximum96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.9 MiB
2024-09-15T15:24:05.797350image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile23
Q133
median45
Q358
95-th percentile81
Maximum96
Range81
Interquartile range (IQR)25

Descriptive statistics

Standard deviation17.378485
Coefficient of variation (CV)0.37179158
Kurtosis-0.1763463
Mean46.742545
Median Absolute Deviation (MAD)12
Skewness0.61235845
Sum60609890
Variance302.01173
MonotonicityNot monotonic
2024-09-15T15:24:05.961328image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
48 45483
 
3.5%
36 40038
 
3.1%
33 37481
 
2.9%
35 37313
 
2.9%
46 34299
 
2.6%
44 32706
 
2.5%
34 31851
 
2.5%
30 31386
 
2.4%
47 31271
 
2.4%
32 30718
 
2.4%
Other values (71) 944129
72.8%
ValueCountFrequency (%)
15 1959
 
0.2%
16 7496
 
0.6%
17 3975
 
0.3%
19 5603
 
0.4%
20 9530
 
0.7%
21 18827
1.5%
22 13241
1.0%
23 29689
2.3%
24 6008
 
0.5%
25 20573
1.6%
ValueCountFrequency (%)
96 536
 
< 0.1%
95 11
 
< 0.1%
94 6063
0.5%
93 4645
0.4%
92 4131
0.3%
91 6224
0.5%
90 3605
0.3%
89 4610
0.4%
88 2096
 
0.2%
87 3041
0.2%

merchant_encoded
Real number (ℝ)

HIGH CORRELATION 

Distinct693
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean342.85849
Minimum0
Maximum692
Zeros1844
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size4.9 MiB
2024-09-15T15:24:06.119063image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile33
Q1165
median346
Q3514
95-th percentile659
Maximum692
Range692
Interquartile range (IQR)349

Descriptive statistics

Standard deviation200.9519
Coefficient of variation (CV)0.58610739
Kurtosis-1.215053
Mean342.85849
Median Absolute Deviation (MAD)175
Skewness0.0086598641
Sum4.4457604 × 108
Variance40381.666
MonotonicityNot monotonic
2024-09-15T15:24:06.277025image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
316 4403
 
0.3%
105 3649
 
0.3%
571 3634
 
0.3%
349 3510
 
0.3%
70 3493
 
0.3%
136 3434
 
0.3%
117 2736
 
0.2%
358 2734
 
0.2%
463 2723
 
0.2%
607 2721
 
0.2%
Other values (683) 1263638
97.5%
ValueCountFrequency (%)
0 1844
0.1%
1 1763
0.1%
2 1751
0.1%
3 1895
0.1%
4 940
 
0.1%
5 1746
0.1%
6 1904
0.1%
7 2503
0.2%
8 1923
0.1%
9 821
 
0.1%
ValueCountFrequency (%)
692 1783
0.1%
691 2560
0.2%
690 1695
0.1%
689 1804
0.1%
688 1297
0.1%
687 2017
0.2%
686 1870
0.1%
685 1766
0.1%
684 1872
0.1%
683 2358
0.2%

time_diff
Real number (ℝ)

Distinct158975
Distinct (%)12.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32460.389
Minimum0
Maximum1341471
Zeros1003
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size9.9 MiB
2024-09-15T15:24:06.437373image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile969
Q16004
median16563
Q340239
95-th percentile113905
Maximum1341471
Range1341471
Interquartile range (IQR)34235

Descriptive statistics

Standard deviation47331.145
Coefficient of variation (CV)1.4581201
Kurtosis31.873749
Mean32460.389
Median Absolute Deviation (MAD)12924
Skewness4.2732438
Sum4.2090574 × 1010
Variance2.2402373 × 109
MonotonicityNot monotonic
2024-09-15T15:24:06.603215image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1003
 
0.1%
221 91
 
< 0.1%
290 90
 
< 0.1%
118 90
 
< 0.1%
445 89
 
< 0.1%
572 88
 
< 0.1%
821 87
 
< 0.1%
11 87
 
< 0.1%
136 86
 
< 0.1%
379 86
 
< 0.1%
Other values (158965) 1294878
99.9%
ValueCountFrequency (%)
0 1003
0.1%
1 62
 
< 0.1%
2 81
 
< 0.1%
3 76
 
< 0.1%
4 57
 
< 0.1%
5 67
 
< 0.1%
6 67
 
< 0.1%
7 64
 
< 0.1%
8 72
 
< 0.1%
9 72
 
< 0.1%
ValueCountFrequency (%)
1341471 1
< 0.1%
1205687 1
< 0.1%
1107569 1
< 0.1%
1096094 1
< 0.1%
1060731 1
< 0.1%
1053269 1
< 0.1%
1045690 1
< 0.1%
1039152 1
< 0.1%
1032247 1
< 0.1%
1016241 1
< 0.1%

day_of_month
Real number (ℝ)

HIGH CORRELATION 

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.589412
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.9 MiB
2024-09-15T15:24:06.740159image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median15
Q323
95-th percentile30
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.8312176
Coefficient of variation (CV)0.56648818
Kurtosis-1.1868502
Mean15.589412
Median Absolute Deviation (MAD)8
Skewness0.031380011
Sum20214401
Variance77.990405
MonotonicityNot monotonic
2024-09-15T15:24:06.874042image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
1 47089
 
3.6%
15 46213
 
3.6%
8 46201
 
3.6%
16 44894
 
3.5%
2 44748
 
3.5%
9 44685
 
3.4%
7 44239
 
3.4%
14 44015
 
3.4%
17 42272
 
3.3%
22 42061
 
3.2%
Other values (21) 850258
65.6%
ValueCountFrequency (%)
1 47089
3.6%
2 44748
3.5%
3 41842
3.2%
4 41479
3.2%
5 41886
3.2%
6 41420
3.2%
7 44239
3.4%
8 46201
3.6%
9 44685
3.4%
10 41934
3.2%
ValueCountFrequency (%)
31 24701
1.9%
30 41019
3.2%
29 41476
3.2%
28 41611
3.2%
27 39684
3.1%
26 40692
3.1%
25 40374
3.1%
24 41360
3.2%
23 40815
3.1%
22 42061
3.2%

is_weekend
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.9 MiB
0
845139 
1
451536 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1296675
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 845139
65.2%
1 451536
34.8%

Length

2024-09-15T15:24:07.007465image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-15T15:24:07.112122image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 845139
65.2%
1 451536
34.8%

Most occurring characters

ValueCountFrequency (%)
0 845139
65.2%
1 451536
34.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1296675
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 845139
65.2%
1 451536
34.8%

Most occurring scripts

ValueCountFrequency (%)
Common 1296675
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 845139
65.2%
1 451536
34.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1296675
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 845139
65.2%
1 451536
34.8%

amt_ratio
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct1166530
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1
Minimum0.0086933687
Maximum386.48159
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.9 MiB
2024-09-15T15:24:07.240941image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.0086933687
5-th percentile0.037613054
Q10.15441537
median0.66941509
Q31.187412
95-th percentile2.620873
Maximum386.48159
Range386.47289
Interquartile range (IQR)1.0329966

Descriptive statistics

Standard deviation2.2999175
Coefficient of variation (CV)2.2999175
Kurtosis2845.425
Mean1
Median Absolute Deviation (MAD)0.51563683
Skewness34.809554
Sum1296675
Variance5.2896205
MonotonicityNot monotonic
2024-09-15T15:24:07.397477image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1697644044 9
 
< 0.1%
0.1066987452 9
 
< 0.1%
0.08787319111 8
 
< 0.1%
0.0790465957 8
 
< 0.1%
0.1549432174 8
 
< 0.1%
0.04983253947 7
 
< 0.1%
0.08567179375 7
 
< 0.1%
0.05196871342 7
 
< 0.1%
0.07718171096 7
 
< 0.1%
0.02356862305 7
 
< 0.1%
Other values (1166520) 1296598
> 99.9%
ValueCountFrequency (%)
0.008693368698 1
< 0.1%
0.00877907162 1
< 0.1%
0.00929940348 1
< 0.1%
0.009387522129 1
< 0.1%
0.009534706331 1
< 0.1%
0.009548024185 2
< 0.1%
0.009568974574 1
< 0.1%
0.009641632266 1
< 0.1%
0.009828848426 1
< 0.1%
0.009856043811 1
< 0.1%
ValueCountFrequency (%)
386.4815883 1
< 0.1%
358.5962394 1
< 0.1%
283.0207865 1
< 0.1%
248.4744115 1
< 0.1%
236.1516822 1
< 0.1%
235.729112 1
< 0.1%
235.4992914 1
< 0.1%
233.4279738 1
< 0.1%
219.4420112 1
< 0.1%
217.5342348 1
< 0.1%

high_value
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.9 MiB
0
1231853 
1
 
64822

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1296675
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1231853
95.0%
1 64822
 
5.0%

Length

2024-09-15T15:24:07.539464image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-15T15:24:07.642072image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 1231853
95.0%
1 64822
 
5.0%

Most occurring characters

ValueCountFrequency (%)
0 1231853
95.0%
1 64822
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1296675
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1231853
95.0%
1 64822
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1296675
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1231853
95.0%
1 64822
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1296675
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1231853
95.0%
1 64822
 
5.0%

amt_merchant_interaction
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct850172
Distinct (%)65.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24035.621
Minimum0
Maximum15748202
Zeros1844
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size9.9 MiB
2024-09-15T15:24:07.762476image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile382.3
Q12552.1
median10352.25
Q329331.085
95-th percentile77275.25
Maximum15748202
Range15748202
Interquartile range (IQR)26778.985

Descriptive statistics

Standard deviation64788.516
Coefficient of variation (CV)2.6955208
Kurtosis6863.1684
Mean24035.621
Median Absolute Deviation (MAD)9173.73
Skewness50.346708
Sum3.1166389 × 1010
Variance4.1975518 × 109
MonotonicityNot monotonic
2024-09-15T15:24:07.931675image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1844
 
0.1%
672 36
 
< 0.1%
1224 36
 
< 0.1%
756 35
 
< 0.1%
693 32
 
< 0.1%
1344 31
 
< 0.1%
1056 31
 
< 0.1%
1512 31
 
< 0.1%
1008 30
 
< 0.1%
315 30
 
< 0.1%
Other values (850162) 1294539
99.8%
ValueCountFrequency (%)
0 1844
0.1%
1 1
 
< 0.1%
1.01 1
 
< 0.1%
1.03 1
 
< 0.1%
1.04 1
 
< 0.1%
1.05 2
 
< 0.1%
1.06 2
 
< 0.1%
1.1 1
 
< 0.1%
1.11 3
 
< 0.1%
1.13 1
 
< 0.1%
ValueCountFrequency (%)
15748201.6 1
< 0.1%
11832531.84 1
< 0.1%
11715740.64 1
< 0.1%
11114186.04 1
< 0.1%
7866051.3 1
< 0.1%
7668280.95 1
< 0.1%
7448548.25 1
< 0.1%
7414617.32 1
< 0.1%
7126857.95 1
< 0.1%
6743415.28 1
< 0.1%

amt_day_interaction
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct99344
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean215.66738
Minimum0
Maximum173693.4
Zeros254282
Zeros (%)19.6%
Negative0
Negative (%)0.0%
Memory size9.9 MiB
2024-09-15T15:24:08.095123image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q18.25
median73.29
Q3269.75
95-th percentile741.42
Maximum173693.4
Range173693.4
Interquartile range (IQR)261.5

Descriptive statistics

Standard deviation631.42542
Coefficient of variation (CV)2.9277743
Kurtosis10492.695
Mean215.66738
Median Absolute Deviation (MAD)73.29
Skewness62.287145
Sum2.796505 × 108
Variance398698.06
MonotonicityNot monotonic
2024-09-15T15:24:08.275882image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 254282
 
19.6%
9 355
 
< 0.1%
7.5 334
 
< 0.1%
10.2 314
 
< 0.1%
12 313
 
< 0.1%
10.8 311
 
< 0.1%
6.24 301
 
< 0.1%
6 296
 
< 0.1%
9.48 291
 
< 0.1%
15 288
 
< 0.1%
Other values (99334) 1039590
80.2%
ValueCountFrequency (%)
0 254282
19.6%
1 36
 
< 0.1%
1.01 67
 
< 0.1%
1.02 62
 
< 0.1%
1.03 70
 
< 0.1%
1.04 53
 
< 0.1%
1.05 59
 
< 0.1%
1.06 60
 
< 0.1%
1.07 55
 
< 0.1%
1.08 65
 
< 0.1%
ValueCountFrequency (%)
173693.4 1
< 0.1%
135598.85 1
< 0.1%
132720.6 1
< 0.1%
107383.44 1
< 0.1%
100347.76 1
< 0.1%
90282.18 1
< 0.1%
76725.06 1
< 0.1%
72338.2 1
< 0.1%
72036.25 1
< 0.1%
67684.2 1
< 0.1%

amt_mean
Real number (ℝ)

Distinct983
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.351035
Minimum42.951671
Maximum948.81818
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.9 MiB
2024-09-15T15:24:08.502688image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum42.951671
5-th percentile52.795807
Q159.813649
median65.09374
Q383.277582
95-th percentile96.281225
Maximum948.81818
Range905.86651
Interquartile range (IQR)23.463933

Descriptive statistics

Standard deviation19.410291
Coefficient of variation (CV)0.27590625
Kurtosis459.14005
Mean70.351035
Median Absolute Deviation (MAD)7.1445792
Skewness14.40415
Sum91222429
Variance376.75938
MonotonicityNot monotonic
2024-09-15T15:24:08.658337image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
61.45309958 3123
 
0.2%
55.35335575 3123
 
0.2%
89.77494389 3119
 
0.2%
57.5451941 3117
 
0.2%
48.47894957 3113
 
0.2%
52.44669344 3112
 
0.2%
95.31727653 3110
 
0.2%
52.78476344 3107
 
0.2%
91.44027688 3106
 
0.2%
89.6934118 3101
 
0.2%
Other values (973) 1265544
97.6%
ValueCountFrequency (%)
42.951671 1538
0.1%
44.71734531 501
 
< 0.1%
46.88654145 1544
0.1%
46.93667091 1571
0.1%
46.98265945 2587
0.2%
47.17054602 2564
0.2%
47.18054799 3011
0.2%
47.94510046 2588
0.2%
48.47894957 3113
0.2%
48.48821547 2107
0.2%
ValueCountFrequency (%)
948.8181818 11
< 0.1%
918.4255556 9
< 0.1%
874.5057143 7
< 0.1%
858.48 8
< 0.1%
842.23125 8
< 0.1%
833.969 10
< 0.1%
810.2785714 7
< 0.1%
799.2133333 9
< 0.1%
778.5718182 11
< 0.1%
774.74375 8
< 0.1%

amt_std
Real number (ℝ)

Distinct983
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean141.34551
Minimum60.247108
Maximum1202.988
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.9 MiB
2024-09-15T15:24:08.810143image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum60.247108
5-th percentile83.708636
Q1103.59066
median122.37808
Q3149.71688
95-th percentile266.56177
Maximum1202.988
Range1142.7409
Interquartile range (IQR)46.126219

Descriptive statistics

Standard deviation73.303097
Coefficient of variation (CV)0.51860931
Kurtosis46.700004
Mean141.34551
Median Absolute Deviation (MAD)21.853523
Skewness5.0391525
Sum1.8327919 × 108
Variance5373.344
MonotonicityNot monotonic
2024-09-15T15:24:08.960417image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
143.1626102 3123
 
0.2%
121.6611149 3123
 
0.2%
118.4502102 3119
 
0.2%
211.4021978 3117
 
0.2%
131.566918 3113
 
0.2%
155.4128126 3112
 
0.2%
216.5906992 3110
 
0.2%
140.3830389 3107
 
0.2%
132.7622028 3106
 
0.2%
137.017678 3101
 
0.2%
Other values (973) 1265544
97.6%
ValueCountFrequency (%)
60.24710813 504
 
< 0.1%
64.15872518 471
 
< 0.1%
65.34697836 518
 
< 0.1%
65.53295778 972
0.1%
65.84396856 496
 
< 0.1%
66.98899721 525
 
< 0.1%
67.49926144 1494
0.1%
70.82320784 1005
0.1%
71.420119 485
 
< 0.1%
72.37512206 509
 
< 0.1%
ValueCountFrequency (%)
1202.988005 510
 
< 0.1%
1165.824421 520
 
< 0.1%
867.2878545 1017
 
0.1%
644.2690528 2050
0.2%
623.2522115 540
 
< 0.1%
543.2532612 13
 
< 0.1%
512.7832113 550
 
< 0.1%
510.8874515 2597
0.2%
483.3276455 1060
0.1%
482.145942 10
 
< 0.1%

Interactions

2024-09-15T15:23:51.308734image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:22:08.832325image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:22:15.079031image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:22:20.229103image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:22:25.228054image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:22:30.324462image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:22:35.736564image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:22:41.032392image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:22:46.188481image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:22:51.180111image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:22:56.067880image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:01.000375image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:06.222333image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:10.915944image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:15.875101image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:20.871856image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:25.818252image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:30.582777image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:35.402488image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:41.079316image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:46.091386image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:51.543238image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:22:09.077137image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:22:15.309282image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:22:20.459859image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:22:25.465940image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:22:30.557861image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:22:36.010879image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:22:41.271848image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:22:46.416520image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:22:51.413740image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:22:56.292931image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:01.221420image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:06.443721image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:11.153006image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:16.141492image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:21.104887image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:26.064238image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:30.805427image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:35.630008image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:41.301822image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:46.319549image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:51.799177image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:22:10.562898image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:22:15.577677image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:22:20.686042image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:22:25.703561image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:22:30.789956image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:22:36.257044image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:22:41.550355image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:22:46.649564image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:22:51.652953image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:22:56.523259image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:01.445833image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:06.664560image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:11.390968image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:16.377520image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:21.332444image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:26.285100image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:31.055286image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:35.860205image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:41.530667image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:46.578226image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:52.077357image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:22:10.781559image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:22:15.817482image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:22:20.920727image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:22:25.928992image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:22:31.047498image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:22:36.509955image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:22:41.778370image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:22:46.870973image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:22:51.882908image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:22:56.748565image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:01.659599image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:06.881598image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:11.622742image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:16.603306image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:21.554770image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:26.501838image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:31.272628image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:36.096370image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:41.749286image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:46.813875image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:52.313802image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:22:11.044122image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:22:16.062299image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:22:21.160200image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:22:26.173874image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:22:31.263892image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:22:36.752043image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:22:42.039617image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:22:47.123918image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:22:52.116817image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:22:57.002087image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:01.880044image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:07.099558image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:11.849908image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:16.836212image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:21.778726image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:26.721929image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:31.488565image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:36.320383image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:42.003372image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:47.056072image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:52.550276image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:22:11.257271image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:22:16.293108image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:22:21.383456image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:22:26.400539image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:22:31.486288image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:22:37.006176image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:22:42.295781image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:22:47.337259image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:22:52.341061image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:22:57.219139image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:02.096196image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:07.308699image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:12.090085image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:17.073904image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:22.034690image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-09-15T15:22:44.750251image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:22:49.714311image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:22:54.634193image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:22:59.552001image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:04.864648image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:09.541710image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:14.428741image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:19.433028image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:24.379493image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:29.210089image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:33.987120image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:39.602984image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:44.539122image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:49.788945image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:55.280597image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:22:13.793019image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:22:19.021122image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:22:24.024905image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:22:29.120555image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:22:34.531139image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:22:39.713099image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:22:45.005375image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:22:49.961565image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:22:54.857202image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:22:59.783656image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:05.083160image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:09.772186image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:14.673602image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:19.667835image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:24.610729image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:29.427025image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:34.214060image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:39.840637image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:44.776848image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:50.052688image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:55.538216image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:22:14.064846image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:22:19.260425image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:22:24.263211image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:22:29.362181image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:22:34.770712image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:22:39.975117image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:22:45.239090image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:22:50.215680image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:22:55.114396image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:00.049453image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:05.306335image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:10.032539image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:14.917029image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:19.922374image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:24.856341image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:29.649882image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:34.446619image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:40.092735image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:45.064103image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:50.303141image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:55.800495image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:22:14.313390image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:22:19.506955image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:22:24.510978image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:22:29.610799image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:22:35.046821image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:22:40.254775image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:22:45.485619image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:22:50.458236image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:22:55.360498image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:00.296483image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:05.536686image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:10.257529image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:15.177808image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:20.186242image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:25.114680image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:29.881777image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:34.694126image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:40.335677image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:45.319396image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:50.551988image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:56.078075image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:22:14.573170image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:22:19.749453image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:22:24.747729image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:22:29.851062image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:22:35.285322image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:22:40.499509image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:22:45.725573image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:22:50.697734image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:22:55.600475image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:00.541460image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:05.763259image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:10.483434image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:15.417819image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:20.426572image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:25.365547image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:30.155967image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:34.951126image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:40.579424image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:45.587865image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:50.802927image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:56.300802image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:22:14.800806image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:22:20.002849image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:22:25.002449image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:22:30.101329image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:22:35.507905image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:22:40.773464image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:22:45.965348image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:22:50.921371image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:22:55.831192image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:00.767271image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:06.005112image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:10.695289image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:15.649184image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:20.646771image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:25.597448image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:30.370291image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:35.177391image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:40.815093image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:45.828570image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T15:23:51.068753image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2024-09-15T15:24:09.092870image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ageamtamt_day_interactionamt_meanamt_merchant_interactionamt_ratioamt_stdcitydayday_of_monthday_of_weekfirstgenderhigh_valueis_fraudis_weekendjoblastmerch_latmerch_longmerchant_encodedstatestreettime_diffunix_time
age1.000-0.024-0.0220.009-0.025-0.006-0.058-0.0120.0010.001-0.013-0.0030.1320.0430.0200.046-0.0260.0170.036-0.020-0.007-0.0570.0490.125-0.004
amt-0.0241.0000.5790.2270.8110.979-0.086-0.0020.0000.000-0.001-0.0020.0000.0740.0000.001-0.010-0.0080.0120.000-0.0120.0100.0080.0300.001
amt_day_interaction-0.0220.5791.0000.1310.4790.570-0.051-0.0030.0180.0180.684-0.0020.0010.0460.0000.008-0.005-0.0040.0080.001-0.0060.0070.0050.062-0.018
amt_mean0.0090.2270.1311.0000.1880.0570.0540.004-0.000-0.000-0.003-0.0390.0100.0850.3130.005-0.010-0.052-0.0250.0070.004-0.0000.0030.0640.002
amt_merchant_interaction-0.0250.8110.4790.1881.0000.798-0.069-0.0010.0000.000-0.000-0.0030.0000.0530.0000.000-0.008-0.0070.009-0.0010.5000.0080.0060.026-0.000
amt_ratio-0.0060.9790.5700.0570.7981.000-0.076-0.0000.0000.0000.0000.0030.0000.0860.0000.001-0.007-0.0000.011-0.001-0.0130.0040.0050.0250.000
amt_std-0.058-0.086-0.0510.054-0.069-0.0761.000-0.0270.0010.001-0.002-0.0510.1120.0080.0440.0030.072-0.037-0.0690.0370.0020.019-0.025-0.040-0.003
city-0.012-0.002-0.0030.004-0.001-0.000-0.0271.000-0.000-0.000-0.001-0.0120.0570.0220.0050.0070.0210.019-0.041-0.067-0.000-0.043-0.023-0.010-0.000
day0.0010.0000.018-0.0000.0000.0000.001-0.0001.0001.0000.0170.0000.0000.0020.0090.070-0.001-0.000-0.0000.000-0.001-0.001-0.001-0.0020.019
day_of_month0.0010.0000.018-0.0000.0000.0000.001-0.0001.0001.0000.0170.0000.0000.0020.0090.069-0.001-0.000-0.0000.000-0.001-0.001-0.001-0.0020.019
day_of_week-0.013-0.0010.684-0.003-0.0000.000-0.002-0.0010.0170.0171.000-0.0000.0060.0020.0121.0000.0010.0040.0000.0010.0010.001-0.003-0.011-0.029
first-0.003-0.002-0.002-0.039-0.0030.003-0.051-0.0120.0000.000-0.0001.0000.2000.0240.0060.005-0.040-0.0580.108-0.030-0.0020.041-0.045-0.0130.001
gender0.1320.0000.0010.0100.0000.0000.1120.0570.0000.0000.0060.2001.0000.0470.0080.0040.1430.0890.1030.0820.0060.0820.1340.0310.000
high_value0.0430.0740.0460.0850.0530.0860.0080.0220.0020.0020.0020.0240.0471.0000.2490.0010.0140.0150.0220.0130.0230.0240.0150.0070.003
is_fraud0.0200.0000.0000.3130.0000.0000.0440.0050.0090.0090.0120.0060.0080.2491.0000.0040.0050.0040.0080.0050.0100.0040.0050.0100.018
is_weekend0.0460.0010.0080.0050.0000.0010.0030.0070.0700.0691.0000.0050.0040.0010.0041.0000.0070.0100.0040.0050.0010.0030.0080.0250.083
job-0.026-0.010-0.005-0.010-0.008-0.0070.0720.021-0.001-0.0010.001-0.0400.1430.0140.0050.0071.000-0.024-0.013-0.0060.0010.042-0.021-0.017-0.000
last0.017-0.008-0.004-0.052-0.007-0.000-0.0370.019-0.000-0.0000.004-0.0580.0890.0150.0040.010-0.0241.000-0.019-0.039-0.000-0.0510.004-0.0070.000
merch_lat0.0360.0120.008-0.0250.0090.011-0.069-0.041-0.000-0.0000.0000.1080.1030.0220.0080.004-0.013-0.0191.0000.104-0.0020.194-0.0050.0120.001
merch_long-0.0200.0000.0010.007-0.001-0.0010.037-0.0670.0000.0000.001-0.0300.0820.0130.0050.005-0.006-0.0390.1041.000-0.0010.0900.0740.006-0.001
merchant_encoded-0.007-0.012-0.0060.0040.500-0.0130.002-0.000-0.001-0.0010.001-0.0020.0060.0230.0100.0010.001-0.000-0.002-0.0011.000-0.0000.001-0.000-0.001
state-0.0570.0100.007-0.0000.0080.0040.019-0.043-0.001-0.0010.0010.0410.0820.0240.0040.0030.042-0.0510.1940.090-0.0001.0000.009-0.0120.001
street0.0490.0080.0050.0030.0060.005-0.025-0.023-0.001-0.001-0.003-0.0450.1340.0150.0050.008-0.0210.004-0.0050.0740.0010.0091.0000.003-0.001
time_diff0.1250.0300.0620.0640.0260.025-0.040-0.010-0.002-0.002-0.011-0.0130.0310.0070.0100.025-0.017-0.0070.0120.006-0.000-0.0120.0031.000-0.035
unix_time-0.0040.001-0.0180.002-0.0000.000-0.003-0.0000.0190.019-0.0290.0010.0000.0030.0180.083-0.0000.0000.001-0.001-0.0010.001-0.001-0.0351.000

Missing values

2024-09-15T15:23:56.509178image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-09-15T15:23:57.902677image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

amtfirstlastgenderstreetcitystatejobunix_timemerch_latmerch_longis_frauddayday_of_weekagemerchant_encodedtime_diffday_of_monthis_weekendamt_ratiohigh_valueamt_merchant_interactionamt_day_interactionamt_meanamt_std
04.9716218056852627370132537601836.011293-82.048315011325140.0100.05686902554.584.9787.393215126.596221
1107.23309157043561247428132537604449.159047-118.186462011422410.0101.987606025842.43107.2353.949320118.337621
2220.11115381160246813307132537605143.150704-112.154481011583900.0103.341580185842.90220.1165.870040101.585754
345.0016346319308426328132537607647.034331-112.561071011533600.0100.618330016200.0045.0072.776673148.593473
441.96336149141821645116132537618638.674999-78.632459011342970.0100.440858012462.1241.9695.17809189.133972
594.6316282047122338479132537624840.653382-76.152667011596070.0101.446905057440.4194.6365.401685110.658809
644.5419936008823511629132537628237.162705-100.153370011275340.0100.493300023784.3644.5490.289835129.427131
771.65311467122423645127132537630838.948089-78.540296011731070.0101.04392607666.5571.6568.635163113.994926
84.2714470068547438375132537631840.351813-79.958146011792500.0100.06225201067.504.2768.591883117.228359
9198.392393021314942329132537636137.179198-87.485381011465630.0102.1073541111693.57198.3994.141775133.033890
amtfirstlastgenderstreetcitystatejobunix_timemerch_latmerch_longis_frauddayday_of_weekagemerchant_encodedtime_diffday_of_monthis_weekendamt_ratiohigh_valueamt_merchant_interactionamt_day_interactionamt_meanamt_std
129666572.17152207173765122147137181652244.938461-83.99623402162621110659.02110.762142015227.87433.0294.69368189.230833
12966667.3011245063563814374137181656240.556811-88.0923390216162746401.02110.10169102000.2043.8071.78608672.979539
129666719.717412419603179186137181665627.465871-81.51180402162922113811.02110.28994104355.91118.2667.979325210.676579
1296668100.8522594074426825196137181668331.377697-90.5284500216364245451.02111.130780042760.40605.1089.186195124.338770
129666937.38228345048358429311137181669641.728638-99.03966002164059872413.02110.614222022353.24224.2860.857433151.348005
129667015.56121332115433044215137181672836.841266-111.69076502165949916781.02110.24627207764.4493.3663.18227498.227403
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